Clinicians and other medical professionals have long relied on auscultatory sounds to aid in the detection and diagnosis of physiological conditions. For example, a clinician may utilize a stethoscope to monitor heart sounds to detect cardiac diseases. As other examples, a clinician may monitor sounds associated with the lungs or abdomen of a patient to detect respiratory or gastrointestinal conditions.
Automated devices have been developed that apply algorithms to electronically recorded auscultatory sounds. One example is an automated blood-pressure monitoring device. Other examples include analysis systems that attempt to automatically detect physiological conditions based on the analysis of auscultatory sounds. For example, artificial neural networks have been discussed as one possible mechanism for analyzing auscultatory sounds and providing an automated diagnosis or suggested diagnosis.
Using these conventional techniques, it is often difficult to provide an automated diagnosis of a specific physiological condition based on auscultatory sounds with any degree of accuracy. Moreover, it is often difficult to implement the conventional techniques in a manner that may be applied in real-time or pseudo real-time to aid the clinician.